Distributionally Robust Optimization in Possibilistic Setting

被引：0

作者：

Guillaume, Romain ^{[1
]}

Kasperski, Adam ^{[2
]}

Zielinski, Pawel ^{[2
]}

机构：

[1] Univ Toulouse IRIT, Toulouse, France

[2] Wroclaw Univ Sci & Technol, Wroclaw, Poland

来源：

IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE) | 2021年

关键词：

robust optimization; possibility theory; imprecise probabilities; fuzzy intervals; FUZZY; UNCERTAINTY; DUALITY;

D O I：

10.1109/FUZZ45933.2021.9494390

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a class of optimization problems with uncertain constraint coefficients is discussed. Namely, for each ill-known coefficient a possibility distribution, being a membership function of a fuzzy interval, is specified. In a possibilistic interpretation, the induced possibility distribution in the set of constraint coefficient realizations encodes a family of probability distributions in this set. The distributionally robust approach is then used to transform imprecise constraints into crisp counterparts. An extension of the model is proposed, in which individual risk aversion of decision makers is taken into account.

引用

页数：6

共 33 条

[1]

[Anonymous], 2012, MANAGING SAFETY HETE

[2]

[Anonymous], 2002, Generalized Concavity in Fuzzy Optimization and Decision Analysis

[3] Practical representations of incomplete probabilistic knowledge [J].